A framework for manufacturing features recognition using a Neural network trained by PSO Algorithm

Author

Shao, Xinyu ; Chen, Zhimin ; Gao, Liang

Author_Institution

Dept. of Ind. & Manuf. Syst. Eng., Huazhong Univ. of Sci. & Tech., Wuhan

Volume

2

fYear

2006

fDate

4-6 Oct. 2006

Firstpage

1371

Lastpage

1374

Abstract

Recently, the rule-based approach, the graph-based approach, the hint-based approach, the artificial neural networks based approach and the volume decomposition approach are the common feature recognition techniques available today. This work discusses a neural network approach for features recognition from B-rep solid modeler, which has significant effect on improving working efficiency in the product life cycle. PSO algorithm is applied to train the neural network. The PSO based NN training algorithm can converge faster and more easily achieve a global minimum

Keywords

CAD/CAM; feature extraction; graph theory; learning (artificial intelligence); manufacturing systems; neural nets; particle swarm optimisation; product life cycle management; B-rep solid modeler; PSO Algorithm; artificial neural networks; graph-based approach; hint-based approach; manufacturing feature recognition; neural network training; particle swarm optimization; product life cycle; rule-based approach; volume decomposition; Active appearance model; Artificial neural networks; Character recognition; Face recognition; Feature extraction; Manufacturing; Neural networks; Solid modeling; Systems engineering and theory; Tree graphs; Neural network; PSO Algorithm; features recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Engineering in Systems Applications, IMACS Multiconference on

Conference_Location

Beijing

Print_ISBN

7-302-13922-9

Electronic_ISBN

7-900718-14-1

Type

conf

DOI

10.1109/CESA.2006.4281852

Filename

4281852